System, method and computer-accessible medium for the reduction of the dosage of gd-based contrast agent in magnetic resonance imaging

ABSTRACT

An exemplary system, method, and computer-accessible medium for generating a gadolinium (“Gd”) enhanced map(s) of a portion(s) of a patient(s), can include, for example, receiving magnetic resonance imaging (MRI) information of the portion(s), and generating the Gd enhanced map(s) based on the MRI information using a machine learning procedure(s). The Gd enhanced map(s) can be a full dosage Gd enhanced map. The full dosage Gd enhanced map(s) can be a full dosage Gd enhanced cerebral blood volume map(s). The machine learning procedure can be a convolutional neural network. The MRI information can include (i) a low-dosage Gd MRI scan(s), or (ii) a Gd-free MRI scan(s). A Gd contrast can be generated in the Gd enhanced map(s) using a T2-weighted MRI image of the portion(s).

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application relates to and claims priority from U.S. PatentApplication No. 62/890,868, filed on Aug. 23, 2019, U.S. PatentApplication No. 62/977,018, filed on Feb. 14, 2020, and U.S. PatentApplication No. 63/048,937, filed on Jul. 7, 2020, the entiredisclosures of which are incorporated herein by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to magnetic resonance imaging(“MRI”), and more specifically, to exemplary embodiments of an exemplarysystem, method and computer-accessible medium for the reduction of thedosage of Gd-based contrast agent in MRI.

BACKGROUND INFORMATION

MRI is a tool used in clinical practice for the care of patients. Theutility of this diagnostic imaging modality has expanded due to theaddition of gadolinium (“Gd”) based contrast agent, which has expandedits utilization. The role of Gd based contrast for MRI imaging can belargely divided into four major categories: (i) morphologic imaging;(ii) steady-state imaging; (iii) perfusion imaging and (iv)contrast-enhanced MR angiography, which can be used in the brain andother parts of the body. (See, e.g., Reference 1). Specifically,Gd-enhanced MRI can be routinely used to better visualize nearly allneurological disease - - - including strokes, tumors, infections, andneuroinflammation. Moreover, since Gd-enhanced MRI can generate highresolution maps of cerebral blood volume (“CBV”) and cerebral blood flow(“CBF”), both tightly coupled to brain metabolism, Gd-enhanced MRI canbe used as a fMRI tool. (See, e.g., Reference 28). In fact, the use ofGd-enhancement was the first fMRI study published. (See, e.g., Reference40). More recently, because it can be used to generate CBV maps that areboth quantitative and has submillimeter resolution, this CBV-fMRIapproach has been used to detect the earliest stages of Alzheimer'sdisease (see, e.g., Reference 11), schizophrenia (see, e.g., Reference41), and to map the effects of normal aging has on the exemplary brains.(See, e.g., Reference 10).

Despite its significant advantages, Gd-enhanced MRI requires anintravenous (“IV”) injection. Recently, reports of gadolinium retentionin the brain and body after previous exposure to gadolinium basedcontrast agents (“GBCAs”) has brought serious safety concerns in theclinical community. (See, e.g., Reference 2). It is known that GBCAscannot be administered to certain patients, such as patients with renalinsufficiency that cannot filter the gadolinium from their body. (See,e.g., Reference 3). Studies have also shown that GBCAs deposition can beindependent of renal function (see, e.g., Reference 4) and higher dosageGBCAs can link to diseases, such as nephrogenic systemic fibrosis(“NSF”) development. (See, e.g., Reference 5). Particularly, patientswho need repeated contrast administration (e.g., multiple sclerosis andbreast cancer screening) are at the highest risk. Further, in 2017, theMedical Imaging Drugs Advisory Committee (“MIDAC”) of the FDArecommended adding a warning to labels about gadolinium retention invarious organs and issued a safety announcement requiring a new classwarning and other safety measures for all GBCAs used for MRI.

Since then the acceptance of GBCA-free procedures has increased inclinical MRI. (See, e.g., Reference 1). A number of methods focusing onGBCA-free perfusion and angiography for brain MRI have been developed,including time-of-fly (“TOF”) angiography, black blood imaging, arterialspin labeling (“ASL”) and vascular-space-occupancy (“VASO”), whichmagnetically ‘label’ protons in the patient's inflowing blood, therebyremoving the need for injection of an exogenous contrast agent. (See,e.g., Reference 6). Multiparametric MRI is another alternative to GBCAs,and some multiparametric MRI methods are already widely used in clinicalpractice. (See, e.g., Reference 7).

Alternatively, there are specific MRI procedures that cannot beperformed with GBCA-free procedures. (See, e.g., References 8-13). Theseinclude MRI imaging to assess neurometabolism, microvascular flow andintegrity, and leakiness. Thus, there is an urgent need to developalternative imaging techniques that reduce the dose of Gd to prevent Gdretention and preserve useful Gd-enhanced contrast information.

Thus, it may be beneficial to provide an exemplary system, method, andcomputer-accessible medium for the reduction of the dosage of Gd-basedcontrast agent in MRI, which can overcome at least some of thedeficiencies described herein above.

SUMMARY OF EXEMPLARY EMBODIMENTS

An exemplary system, method, and computer-accessible medium forgenerating a gadolinium (“Gd”) enhanced map(s) of a portion(s) of apatient(s), can include, for example, receiving magnetic resonanceimaging (MRI) information of the portion(s), and generating the Gdenhanced map(s) based on the MRI information using a machine learningprocedure(s). The Gd enhanced map(s) can be a full dosage Gd enhancedmap. The full dosage Gd enhanced map(s) can be a full dosage Gd enhancedcerebral blood volume map(s). The machine learning procedure can be aconvolutional neural network. The MRI information can include (i) alow-dosage Gd MRI scan(s), or (ii) a Gd-free MRI scan(s). A Gd contrastcan be generated in the Gd enhanced map(s) using a T2-weighted MRI imageof the portion(s).

In some exemplary embodiments of the present disclosure, the machinelearning procedure(s) can include an attention unit(s) and a residualunit(s). The machine learning procedure(s) can include at least fivelayers. The machine learning procedure(s) can include a contractionpath(s) configured to encode a high resolution image(s) into a lowresolution representation(s). The machine learning procedure(s) caninclude an expansion path(s) configured to decode the low resolutionrepresentation(s) into a further high-resolution image(s). The machinelearning procedure(s) can include at least five encoding layers and atleast five decoding layers. Each of the at least five encoding layersand each of the at least five decoding layers can include a residualconnection.

In certain exemplary embodiments of the present disclosure, Each of theat least five encoding layers and each of the at least five decodinglayers can include two series of 3×3 two-dimensional convolutions. Eachof the at least five encoding layers can be followed by a 2×2max-pooling layer, and (ii) each of the at least five decoding layerscan be followed by at least one 2×2 upsampling layers. The machinelearning procedure(s) can include max-pooling and upsampling, and themax-pooling and the upsampling can each be performed using a factor of2. The machine learning procedure(s) can include a batch normalizationlayer(s) and a rectified linear unit layer(s). The portion(s) can be asection(s) of a brain of the patient(s).

These and other objects, features and advantages of the exemplaryembodiments of the present disclosure will become apparent upon readingthe following detailed description of the exemplary embodiments of thepresent disclosure, when taken in conjunction with the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objects, features and advantages of the present disclosure willbecome apparent from the following detailed description taken inconjunction with the accompanying Figures showing illustrativeembodiments of the present disclosure, in which:

FIG. 1 is an exemplary diagram illustrating a T2 weighed MRI acquisitionand the generation of a ΔR2 map according to an exemplary embodiment ofthe present disclosure;

FIG. 2 is an exemplary diagram illustrating the exemplary ResAttU-Netarchitecture according to an exemplary embodiment of the presentdisclosure;

FIG. 3A is a set of exemplary images of Ground truth and DeepCBVpredictions of four different U-Net architectures and 20% low-dosage CBVmap of one slice under the same threshold according to an exemplaryembodiment of the present disclosure;

FIG. 3B is a set of exemplary graphs illustrating the comparison of MSE,SSIM, P.R², S.R², and PSNR according to an exemplary embodiment of thepresent disclosure;

FIG. 4A is an exemplary diagram illustrating the visualization of regionof interest segmentation according to an exemplary embodiment of thepresent disclosure;

FIG. 4B is an exemplary line chart illustrating the region of interestΔR2 of ground truth and ResAttU-Net's predictions and the 20% low-dosageCBV image according to an exemplary embodiment of the presentdisclosure;

FIG. 4C is an exemplary graph illustrating the correlation analysisbetween the predicted results and 20% low-dosage CBV mapping accordingto an exemplary embodiment of the present disclosure;

FIG. 5A is a set of exemplary images illustrating Ground truth andDeepCBV predictions and a 20% low-dosage CBV map of one slice under thesame threshold according to an exemplary embodiment of the presentdisclosure;

FIG. 5B is a set of exemplary graphs illustrating quantitativecomparisons for MSE, SSIM, P.R², S.R², and PSNR between the two input'sDeepCBV results and the 20% low-dosage result according to an exemplaryembodiment of the present disclosure;

FIG. 6A is a set of exemplary images illustrating Ground truth andDeepCBV predictions and 20% low-dosage CBV map of one slice under thesame threshold according to an exemplary embodiment of the presentdisclosure;

FIG. 6B is a set of exemplary graphs illustrating quantitativecomparisons for MSE, SSIM, P.R², S.R², and PSNR between the two input'sDeepCBV results and the 20% low-dosage result according to an exemplaryembodiment of the present disclosure;

FIG. 7A is a set of exemplary images illustrating three-dimensionaltumor regions of Ground truth and DeepCBV predictions and 20% low-dosageCBV map under a same threshold according to an exemplary embodiment ofthe present disclosure;

FIG. 7B is a set of exemplary graphs illustrating quantitativecomparisons for Dice coefficient and Hausdorff distance between the twoinput's DeepCBV results and the 20% low-dosage result according to anexemplary embodiment of the present disclosure;

FIG. 8 is a set of exemplary images illustrating human CBV fMRI dataacquisition and processing pipeline according to an exemplary embodimentof the present disclosure;

FIG. 9A is a set of exemplary images of human brain MRI scans accordingto an exemplary embodiment of the present disclosure;

FIG. 9B is a graph illustrating a region-of-interest correlationanalysis according to an exemplary embodiment of the present disclosure;

FIG. 9C is a set of exemplary images of three-dimensional renderings ofrates of CBV change over time according to an exemplary embodiment ofthe present disclosure;

FIG. 10 is an exemplary flow diagram of an exemplary method forgenerating a gadolinium enhanced map of a portion of a patient accordingto an exemplary embodiment of the present disclosure;

FIG. 11A is a set of exemplary steady-state CBV map derived using theexemplary T2W MRI Pre scans according to an exemplary embodiment of thepresent disclosure;

FIG. 11B is a set of exemplary low dose CBV maps according to anexemplary embodiment of the present disclosure;

FIG. 12 is an exemplary diagram of the exemplary architecture of theexemplary REDAttU network according to an exemplary embodiment of thepresent disclosure;

FIG. 13A is a set of exemplary steady-state ground truth maps accordingto an exemplary embodiment of the present disclosure;

FIG. 13B is a set of exemplary charts illustrating comparisons betweenthe exemplary DeepContrast results and a 20% Gd CBV according to anexemplary embodiment of the present disclosure;

FIG. 14A is a set of exemplary steady-state CBV maps according to anexemplary embodiment of the present disclosure;

FIG. 14B is a set of exemplary charts illustrating a comparison betweenthe exemplary DeepContrast ad a 20% low-dose CBV according to anexemplary embodiment of the present disclosure;

FIG. 14C is a set of exemplary images of tumor regions according to anexemplary embodiment of the present disclosure;

FIG. 14D is a set of exemplary charts illustrating comparisons betweenthe exemplary DeepContrast results and a 20% low-dose CBV according toan exemplary embodiment of the present disclosure;

FIG. 15A is a set of T1W MRI scans before and after Gd enhancementaccording to an exemplary embodiment of the present disclosure;

FIG. 15B is a set of exemplary images illustrating two exemplarypre-processing procedures according to an exemplary embodiment of thepresent disclosure;

FIG. 16 is an exemplary diagram illustrating the exemplary DeepContrastnetwork architecture according to an exemplary embodiment of the presentdisclosure;

FIG. 17A is a set of exemplary images illustrating a similarity analysisbetween CBV and the exemplary DeepContrast using visual inspectionsaccording to an exemplary embodiment of the present disclosure;

FIG. 17B is a set of exemplary charts illustrating a similarity analysisperformed on test scans

FIGS. 18A and 18B are exemplary charts illustrating the populationdistribution used for the exemplary dataset according to an exemplaryembodiment of the present disclosure;

FIG. 19A is a set of exemplary images of 3D renderings of rates ofmetabolic change over time according to an exemplary embodiment of thepresent disclosure;

FIG. 19B is an exemplary chart illustrating a correlation analysis amongt maps according to an exemplary embodiment of the present disclosure;

FIGS. 19C and 19D are exemplary charts illustrating receiver operatingcharacteristics of the 1000-class classification using normalized CBVt-values as ground truths and normalized t-values from either theexemplary DeepContrast or T1W as the predictor according to an exemplaryembodiment of the present disclosure;

FIG. 20 is an exemplary diagram of various research studies according toan exemplary embodiment of the present disclosure;

FIG. 21A is a set of exemplary images of the exemplary DeepContrastprediction according to an exemplary embodiment of the presentdisclosure;

FIGS. 21B-21E are exemplary graphs illustrating a comparison between theexemplary Deep Contrast prediction and the ground truth according to anexemplary embodiment of the present disclosure;

FIG. 21F is a set of exemplary graphs illustrating a tumor segmentationreceiver operating characteristic curve according to an exemplaryembodiment of the present disclosure;

FIG. 22A is a set of further exemplary images of the exemplaryDeepContrast prediction according to an exemplary embodiment of thepresent disclosure;

FIGS. 22B and 22C are sets further exemplary graphs illustrating acomparison between the exemplary Deep Contrast prediction and the groundtruth according to an exemplary embodiment of the present disclosure;

FIG. 23A is an exemplary three-dimensional rendering and a set of imagesof the of the bilateral hippocampal formation according to an exemplaryembodiment of the present disclosure;

FIGS. 23B-23D are sets of CBV-predicted maps and coronal slices on whichthe hippocampal formation mask was applied according to an exemplaryembodiment of the present disclosure;

FIG. 23E is an exemplary scatter plot illustrating the associationbetween age and mean CBV-Predicted values from the dentate gyrusaccording to an exemplary embodiment of the present disclosure;

FIG. 23F is an exemplary box plot illustrating individual-subject meanCBV-Predicted values in the left anterior CA1 according to an exemplaryembodiment of the present disclosure;

FIG. 23G is an exemplary box plot illustrating individual-subject meanCBV-Predicted values in the right transentorhinal cortex according to anexemplary embodiment of the present disclosure;

FIG. 24A is a set of exemplary images of predictions using the exemplaryHuman Tumor Brain Model according to an exemplary embodiment of thepresent disclosure;

FIG. 24B is a set of exemplary graphs illustrating the similaritybetween the exemplary Human Tumor Brain Model and the ground truthaccording to an exemplary embodiment of the present disclosure;

FIG. 24C is a set of exemplary tumor segmentation receiver operatingcharacteristic curves of Gd-Predicted versus T1W according to anexemplary embodiment of the present disclosure;

FIG. 24D is a set of exemplary images of predictions using the exemplarybreast cancer model according to an exemplary embodiment of the presentdisclosure;

FIG. 24E is a set of exemplary graphs illustrating the similaritybetween the exemplary breast cancer model and the ground truth accordingto an exemplary embodiment of the present disclosure;

FIG. 24F is a set of exemplary breast cancer tumor segmentation receiveroperating characteristic curves of Gd-Predicted versus T1W according toan exemplary embodiment of the present disclosure;

FIG. 25 is an exemplary table providing quantitative evaluations of theexemplary Deep Contrast models according to an exemplary embodiment ofthe present disclosure;

FIGS. 26A-26D are exemplary diagrams illustrating various exemplarytrainings of the exemplary DeepContrast models according to an exemplaryembodiment of the present disclosure;

FIG. 27 is an exemplary diagram illustrating a neural networkarchitecture according to an exemplary embodiment of the presentdisclosure;

FIG. 28 is an exemplary diagram illustrating a further neural networkarchitecture according to an exemplary embodiment of the presentdisclosure;

FIG. 29 is an exemplary diagram illustrating an even further neuralnetwork architecture according to an exemplary embodiment of the presentdisclosure;

FIG. 30 is an exemplary diagram illustrating a further neural networkarchitecture according to an exemplary embodiment of the presentdisclosure;

FIG. 31 is a set of exemplary maps illustrating correlation between agadolinium-uptake and gadolinium-predicted over the entire brainaccording to an exemplary embodiment of the present disclosure;

FIG. 32A is an exemplary three-dimensional rendering of the inferiorfrontal gyms, dentate gyrus, and entorhinal cortex overlaid on agroup-wise T1-weighted MRI template according to an exemplary embodimentof the present disclosure;

FIGS. 32B and 32C are sets of exemplary age-related regressions ofcerebral blood volume maps according to an exemplary embodiment of thepresent disclosure;

FIG. 32D is a set of scatter plots of the region of interest-mean CBVvs. CBV-Predicted values according to an exemplary embodiment of thepresent disclosure;

FIG. 33A is a set of exemplary three-dimensional volume renderings ofthe age-related t-score maps according to an exemplary embodiment of thepresent disclosure;

FIG. 33B is an exemplary scatter plot of the age-related t-scoreaccording to an exemplary embodiment of the present disclosure;

FIG. 33C is a set of exemplary graphs illustrating an analysis of theconcordance to CBV t-scores according to an exemplary embodiment of thepresent disclosure;

FIG. 34A is an exemplary three-dimensional rendering of the left CA1overlaid on the left hippocampus, built from a group-wise T1-weightedMRI template according to an exemplary embodiment of the presentdisclosure;

FIGS. 34B and 34C are exemplary slice-based two-sample t-test resultsfor each coronal slice along the anterior-posterior axis of the left(see e.g., FIG. 15B) and right (see e.g., FIG. 15C) CA1 according to anexemplary embodiment of the present disclosure;

FIG. 35 is an exemplary table of public brain magnetic resonance imagingdatabases according to an exemplary embodiment of the presentdisclosure; and

FIG. 36 is an illustration of an exemplary block diagram of an exemplarysystem in accordance with certain exemplary embodiments of the presentdisclosure.

Throughout the drawings, the same reference numerals and characters,unless otherwise stated, are used to denote like features, elements,components, or portions of the illustrated embodiments. Moreover, whilethe present disclosure will now be described in detail with reference tothe figures, it is done so in connection with the illustrativeembodiments and is not limited by the particular embodiments illustratedin the figures.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Procedures that can estimate high-quality Gd contrast directly from thelow-dosage or Gd-free scans can be beneficial. Recently, deep learningmethods have shown great potential in dosage reductions in medicalimaging which can facilitate less radiotracer used in PET (see, e.g.,Reference 14), lower X-ray exposure in CT (see, e.g., Reference 15) andlower Gd dosage in Mill for glioma enhancement. (See, e.g., Reference16). Using artificial intelligence, a full dosage Gd-enhancement can beestimated using reduced Gd. The exemplary system, method andcomputer-accessible medium, according to an exemplary embodiment of thepresent disclosure, can utilize a residual attention U-Net architectureto estimate full dosage Gd-enhanced CBV maps from 20% Gd dosage MRI andfurther to produce Gd contrast directly from T2-weighted (“T2W”) MRIwhile preserving high image quality and contrast information. Theexemplary model was tested in both CBV fMRI scans acquired from labanimals (i.e., wild-type C576J/BL (“WT”) mice and a Glioblastoma (“GBM”)mouse model) and human subjects.

Exemplary Material and Methods

Exemplary Animal Subject: Mice used in the exemplary study included twogroups: WT mice and mice with GBM. The WT group contains 51 healthyadult C576J/BL male mice scanned at 12-14 months age range. The GBMgroup contains 10 adult C576J/BL male mice which were injected withPDGFB(+/+) PTEN(−/−) p53(−/−) GBM cells). Mice GBM cells expressmolecular markers similarly to human proneural GBM cells. 50,000 cellsin 1 μL were stereotactically injected into the brain. MRI scans of GBMmice were obtained 10 days after injection.

Exemplary Human Dataset: MRI scans of human subjects used in theexemplary study included 599 steady-state Gd-enhanced CBV fMRI scansacquired at 3T across multiple sites, vendors, and time points. (See,e.g., References 10, 11, 28 and 41).

Exemplary MRI Acquisition for Animal: For each mouse subject,T2-weighted (“T2W”) MRI scans were acquired using a two-dimensional(“2D”) T2-weighted Turbo Rapid Acquisition with Refocused Echoes(“RARE”) sequence (e.g., TR/TE=3500/45, RARE factor=8, 76 μm in-planeresolution, 450 μm slice thickness) at 9.4T (e.g., Bruker Biospec 94/30USR equipped with CryoProbe). FIG. 1 shows is an exemplary diagramillustrating a T2 weighed MRI acquisition and the generation of a ΔR2map 120 according to an exemplary embodiment of the present disclosure.25 consecutive scans were collected consecutively for 36 minutes, andIntraperitoneal (“IP”) injection of Gadodiamide at 10 mmol/kg wasadministered to the mouse 6 minutes after the initial scan was taken.The first 4 scans were averaged to generate the contrast-free scan(“Pre”) 105, while the low-dosage scan (“Low”) 110 and full-dosage scan(“High”) 115 were generated by averaging the 6th to 9th scans and the33rd to 36th scans, respectively. The gadolinium uptake by the mouse canreach its steady state maximum, (e.g., full-dosage) at around 30 minutesafter IP injection, or in other words, 36 minutes after the initialscan. The uptake can reach approximately 20% of the full-dosage at 4minutes after IP injection, or 10 minutes after the initial scan.

Exemplary MRI Acquisition for Human Subject: For the CBV-fMRI 125 shownin FIG. 1, a steady-state contrast enhanced CBV procedure 130 was used.(See, e.g., References 10 and 11). MRI scans were acquired with aPhilips Achieva 3.0 T MRI scanner using an 8-channel SENSE head coil. Ineach scan session, a T1-weighted structural scan (TR=6.7 ms, TE=3.1 ms,field of view (“FOV”)=240×240×192 mm³, voxel size=0.9×0.9×0.9 mm³) wasfirst acquired using a Turbo Field Echo (“TFE”) gradient echo (“GRE”)sequence; a pair of un-scaled T1-weighted images (TR=7 ms, TE=3 ms,FOV=240×240×162 mm³, voxel size=0.68×0.68×3 mm³) were acquiredafterwards with a bolus injection of gadolinium contrast agent inbetween.

Exemplary Data Preprocessing for Animal: The raw scans from the Brukerscanner were converted to NIfTI format, and for each subject, rigid-bodyspatial normalization was used to align the Pre scan 105, the Low scan110, and the High scan 115. After that, brain extraction using brainmasks (e.g., binary maps) was completed using PCNN3D. (See, e.g.,Reference 17). As shown in FIG. 1, relative CBV ground-truth weregenerated as the ΔR2 maps using the Pre scan 105 and the High scan 115.(See, e.g., Reference 8)). The low-contrast CBV were also generated asthe ΔR2 using Pre and Low scans, which were later used as baselines forcomparison. For scans of tumor subjects, ground truth tumor masks weregenerated in addition to the brain masks using the FCM (e.g.,Fuzzy-C-Means) Clustering Based Segmentation. (See, e.g., Reference 18).

Exemplary Data Preprocessing for Human: The T1-weighted structuralimages were processed using FreeSurfer, generating cortical parcellation(see, e.g., References 42 and 43) and hippocampal subregionssegmentation (see, e.g., Reference 44) in the individual space. Theprimary hippocampal subregions labeled include presubiculum (“PRESUB”),subiculum (“SUB”), CA1, CA3, CA4 (e.g., hilus), granule cell molecularlayer of DG (“DG”), molecular layer of subiculum and CA fields(“MLSUBCA”). The list of cortical regions can be found in theparcellation protocol. (See, e.g., Reference 42).

CBV-fMRI processing followed the previous exemplary procedures (see,e.g., References 10 and 11) and included registration of thepre-contrast and the post-contrast T1-weighted scans, subtraction of theco-registered post-contrast and pre-contrast scans, and CBV valuenormalization with the top 5% mean signal of the whole head regions. Theraw CBV values are % CBV measures in a unit voxel.

Individual structural images were registered into template space withaffine registration. Individual CBV images were linearly registered intothe individual structural image space. The CBV images were registered tothe template space with the transformation field composed of the affinetransformation matrix. In region-of-interest (“ROI”) analyses, mean CBVmeasures the average amount of cerebral blood volume in an anatomicallydefined ROI.

Exemplary Deep Learning Model: As shown in FIG. 1, an exemplary deeplearning network was used to estimate the ground-truth CBV from thePre+Low scans and the Pre scan only respectively. The formercorresponded to a 5-fold reduction of Gd dosage for CBV map generationand the latter corresponded to completely moving away from Gd. Gdcontrast can be produced directly from Gd-free MRI scans and high imagequality and contrast information in CBV fMRI can be preserved. In anexemplary mouse study, both wild-type (“WT”) mice and mice withGlioblastoma (“GBM”) at 9.4T were used. The exemplary DeepCBV procedurewas then tested on a human dataset that included 599 steady-stateGd-enhanced CBV fMRI scans acquired at 3T across multiple sites,vendors, and time points. (See, e.g., References 10, 11, 28 and 41).

The performance of the U-Net with only attention units (“AttU-Net”),U-Net with only residual units (“ResU-Net”) and the U-Net with bothresidual and attention unit (“ResAttU-Net”) were analyzed with the inputof Pre+Low image. The exemplary deep learning architecture included theout-stand five-layer ResAttU-Net as illustrated in FIG. 2. Inparticular, FIG. 2 shows an exemplary diagram illustrating the exemplaryResAttU-Net architecture according to an exemplary embodiment of thepresent disclosure. The exemplary ResAttU-Net architecture includes acontraction path that can encode high resolution data into lowresolution representations, and an expansion path that can decode suchencoded representations back to high-resolution images. The exemplaryResAttU-Net was applied to further evaluate the necessity of the Lowimage inputting during to experiment, and to evaluate whether it can bepossible that the exemplary deep learning approach can derive the CBVmap directly from the Pre image only. The exemplary system, method, andcomputer-accessible medium can utilize sophisticated tumor data for theclinical application, which is described below.

As shown in FIG. 2, the exemplary architecture of ResAttU-Net caninclude of 5 encoding layers 205, 210, 215, 220, and 225 and 5 decodinglayers 230, 235, 240, 245, and 250. All the layers can include aresidual connection 250; the decoding layers 230-250 can be implementedwith attention mechanism. Various Max-pooling can be performed (e.g.,with a factor of 2) as well as upsampling 255 also with a factor of 2.Additionally, Conv2D+Batch Normalization (“BN)+Rectified Linear Unit(“ReLu”) layers 260 can be included. The exemplary network can take bothPre and Low as the two-channel input or only Pre as the single-channelinput. Estimated 2D CBV maps with brain extraction can be the output.

The exemplary study included 51 WT mice scans, with a 39-6-6train-validation-test split. 4 mice with GBM, in addition to the WTmice, were included. 599 human scans were used in the study, with a326-93-180 train-validation-test split. Randomization was performed atthe subject level to prevent the images from the same subject dataoverlapping across sets.

Exemplary Evaluation and Statistical Analysis: To evaluate the exemplarysystem, method, and computer-accessible medium, the mean square error(“MSE”) and peak signal to noise ratio (“PSNR”) were used to assess theestimation error on voxel level, and the structural similarity index(“SSIM”), Pearson correlation coefficient, and Spearman correlationcoefficient to evaluate the accuracy of estimation on the structurallevel. In addition to these quantitative analysis procedures, a Dicesimilarity coefficient and Hausdorff distance were utilized evaluate thetumor model results. (See, e.g., Reference 19).

Exemplary MSE

Given a reference image f and a test image g, both of size M×N, the MSE(see, e.g., Reference 20) between f and g can be defined by, forexample:

${{MSE}\left( {f,g} \right)} = {\frac{1}{MN}{\sum\limits_{i = 1}^{M}\; {\sum\limits_{j = 1}^{N}\; \left( {f_{ij} - g_{ij}} \right)^{2}}}}$

Exemplary PSNR

The exemplary PSNR (see, e.g., Reference 20) between f and g can bedefined by, for example:

${{PSNR}\left( {f,g} \right)} = {10\mspace{14mu} {\log_{10}\left( \frac{255^{2}}{{MSE}\left( {f,g} \right)} \right)}}$

Exemplary SSIM

The exemplary SSIM (see, e.g., Reference 20) between f and g can bedefined by, for example:

${{SSIM}\left( {f,g} \right)} = {{l\left( {f,g} \right)}{c\left( {f,g} \right)}{s\left( {f,g} \right)}\mspace{14mu} {where}\mspace{14mu} \left\{ \begin{matrix}{{l\left( {f,g} \right)} = \frac{{2\mu_{f}\mu_{g}} + C_{1}}{\mu_{f}^{2} + \mu_{g}^{2} + C_{1}}} \\{{c\left( {f,g} \right)} = \frac{{2\sigma_{f}\sigma_{g}} + C_{2}}{\sigma_{f}^{2} + \sigma_{g}^{2} + C_{2}}} \\{{s\left( {f,g} \right)} = \frac{\sigma_{fg} + C_{3}}{{\sigma_{f}\sigma_{g}} + C_{3}}}\end{matrix} \right.}$

Exemplary Pearson Correlation Coefficient (“PCC”)

The exemplary PCC can be defined by, for example:

$r_{xy} = \frac{\sum\limits_{i = 1}^{n}\; {\left( {x_{i} - \overset{\_}{x}} \right)\left( {y_{i} - \overset{\_}{y}} \right)}}{\sqrt{\sum\limits_{i = 1}^{n}\; \left( {x_{i} - \overset{\_}{x}} \right)^{2}}\sqrt{\sum\limits_{i = 1}^{n}\; \left( {y_{i} - \overset{\_}{y}} \right)^{2}}}$

Exemplary Spearman Correlation Coefficient (“SCC”)

The exemplary SCC can be defined by, for example:

$\rho = {1 - \frac{6\mspace{14mu} \Sigma \mspace{14mu} d_{i}^{2}}{n\left( {n^{2} - 1} \right)}}$

where ρ can be the Spearman rank correlation; d_(i) can be thedifference between the ranks of corresponding variables; and n can bethe number of observations.

Exemplary Dice Similarity Coefficient (“DSC”)

The exemplary Dice similarity coefficient can be a simple but powerfulmethod to quantify the spatial overlap. (See, e.g., Reference 39). Inthe exemplary case, X and Y can be the prediction and the ground truthrespectively, and the DSC can be defined by, for example:

${DSC} = \frac{2{{X\bigcap Y}}}{{X} + {Y}}$

Exemplary Hausdorff Distance (d_(H))

The exemplary Hausdorff distance can facilitate the determination of theinterval between two subsets of a metric space. (See, e.g., Reference21). Same as DSC, let X and Y be the prediction and the ground truth ofthe tumor model. Thus, for example:

d _(H)(X,Y)=max{sup _(x∈X) inf _(y∈Y) d(x,y),sup _(y∈Y) inf _(x∈X)d(x,y),}

Application to GBM Mouse Model: A tumor case was used to evaluate theexemplary CBV map. To further probe into the application of the deeplearning network, 6 GBM mice scans were randomly added to the trainingset and the network was tested on other 4 GBM mice scans.

Exemplary Results

Exemplary Quantitative Evaluations of Different U-net Architectures:Different U-net architectures' performance in CBV mapping contrastenhancement were analyzed. The results are shown in FIG. 3. Inparticular, FIG. 3A shows a set of exemplary images of Ground truth andDeepCBV predictions of four different U-Net architectures and 20%low-dosage CBV map of one slice under the same threshold according to anexemplary embodiment of the present disclosure. All four deep learningcontrast results show strong enhancement compared to the 20% low-dosageCBV ΔR2. The similarity between predicted and real high contrast can beobserved. This can be observed from the fine structure of hippocampusand cortex and the contrast between CSF and tissue.

FIG. 3B shows the MSE, SSIM, PSNR and Correlation Coefficientscomparison between different U-net architectures' CBV estimation and 20%low-dosage CBV (e.g., for ResAttU-Net 305, U-Net 310, ResU-Net 315,AttU-Net 320, and 20% Low Dose 325). ResAttU-Net's 305 result shows thelargest SSIM, PSNR, and Correlation Coefficients and the smallest MSE,indicating ResAttU-net's 305 best performance and potential for furtherobjectives. Statistical analyses were performed using paired t-test.Values denote mean±S.E.M. *P<0.05, **P<0.01, ***P<0.001. As shown inFIG. 3B, the exemplary ResAttU-Net outperforms all other architecturesin all evaluations.

In addition, one WT mouse was randomly chosen for the ROI evaluation.The mouse brain atlas, which is shown in the diagram of FIG. 4A,contains 75 ROIs in the cortex, 16 ROIs in the hippocampus, and 35 ROIsin the basal ganglia. FIG. 4B shows an exemplary diagram illustratingthe visualization of region of interest segmentation according to anexemplary embodiment of the present disclosure. In particular, FIG. 4Bshows the similarity of ROI CBV mean between ResAttU-Net Delta R2 405,the 20% Low Dose Delta 410, and the Delta R2 415. The exemplaryResAttU-Net predicted result matches the ground truth well. FIG. 4Cillustrates an exemplary graph illustrating the correlation analysisbetween the predicted results and 20% low-dosage CBV mapping accordingto an exemplary embodiment of the present disclosure. In particular, asshown in FIG. 4C, the correlation coefficient of the ResAttU-Net'sprediction 420, is better than the 20% low dosage ΔR2 map 425. Theexemplary ResAttU-Net map shows much stronger similarity than the 20%low-dosage map compared to the CBV ground truth.

Exemplary Performance Evaluation of DeepCBV in Normal Brian CBV Mappingin Mice

The exemplary ResAttU-Net was evaluated with input training data of justPre image of WH to test whether it can be possible to derive the DeepCBVmap without contrast agents. FIGS. 5A and 5B show the predictions andthe statistic results of the Pre+Low and Pre compared with the 20% Lowdose and Pre-contrast image. In particular, FIG. 5A shows the same slicefrom a subject. Pre image's deep learning contrast results show strongenhancement compared to the 20% low-dosage CBV ΔR2 and the Pre-contrastimage. The similarity between the predictions of the two inputconditions can be observed from the contrast enhanced hippocampus andcortex and the difference between CSF and tissue.

FIG. 5B shows the quantitative comparison of the predictions of Pre+Low(e.g., ResAttU-Net 505), Pre (e.g., ResAttU-Net 510), and 20% low-dosage(e.g., 20% Low Dose 515) CBV ΔR2. The results indicate that when thetraining data contains both Pre and Low image, the results maintainbetter structure information and data relativity (e.g., high Pearsoncorrelation and Spearman correlation). This can be predictable as thelow dose image has information that can contribute to the CBV.Nevertheless, Pre-contrast image alone as input training data can stillprovide a promising performance of CBV estimation. For all testingcases, DeepCBV derived directly from Pre images show significantlyimprovement over the 20% low-dosage CBV on all quantitative metrics,showing the potential of developing a GBCA-free CBV estimation algorithmusing deep learning. Statistical analyses were using paired t-test;Values denote mean±S.E.M. *P<0.05, **P<0.01, ***P<0.001. An enhanced CBVmap can be obtained with just the Pre image as input.

Exemplary Performance of DeepCBV in GBM CBV Enhancement in Mice

The exemplary ResAttU-Net was applied to the GBM mouse model. FIG. 6Ashows the predicted result of one slice of the same tumor subjectaccording to an exemplary embodiment of the present disclosure.Comparing with low dosage CBV, both Pre+Low and Pre image has generatedCBV maps, which have similar contrast as the ground truth. The DeepCBVprediction with input of both the Pre+Low and Pre only image showssignificant contrast enhancement and tumor region prediction compared to20% low-dosage result. FIG. 6B shows that DeepCBV significantlyoutperforms the 20% low-dosage CBV estimation. In particular, FIG. 6Bshows quantitative comparisons (e.g., MSE, SSIM, P.R², S.R², and PSNR)between the two input's DeepCBV results (e.g., ResAttU-Net 605 andResAttU-Net 610) and the 20% low-dosage result (e.g., 20% Low Dose 615).Statistical analyses were using paired t-test; Values denote mean±S.E.M.*P<0.05, **P<0.01, ***P<0.001.

FIG. 7A shows the 3D rendering results of CBV ground truth vs DeepCBV inthe FCM segmented tumor region. Compared with low dosage CBV, both thetwo DeepCBV models derived the tumor region more precisely with asimilar contrast level of the ground truth. FIG. 7B shows a set ofexemplary graphs illustrating quantitative comparisons for Dicecoefficient and Hausdorff distance between the two input's DeepCBVresults and the 20% low-dosage result according to an exemplaryembodiment of the present disclosure. The exemplary models with theinput of both Pre+Low (e.g., ResAttU-Net 705) images and the Pre-only(e.g., ResAttU-Net 710) images outperformed the 20% low-dosage (e.g.,20% Low Dose 715) on both Dice coefficient and Hausdorff distancesignificantly. Statistical analyses were using paired t-test; Valuesdenote mean±S.E.M. *P<0.05, **P<0.01, ***P<0.001. Both the Pre+Low andthe Pre image can predict the tumor region with enhanced contrast andsimilar space feature by ResAttU-Net.

Exemplary Performance of DeepCBV in Human Studies

FIG. 8 illustrates a set of exemplary images illustrating human CBV fMRIdata acquisition and processing pipeline according to an exemplaryembodiment of the present disclosure. T1W structural MRI scans with Gdcontrast enhancement are acquired and CBV ground truths were calculatedas the normalized gadolinium uptake modeled by the change of thelongitudinal T1 relaxation rate (“ΔR1”). The exemplary DeepCBV deeplearning model can be used to predict the CBV mappings solely from theGd-free pre-contrast T1W structural scans. DeepCBV was estimateddirectly from Gd-free T1W pre-contrast scan using the exemplaryResAttU-Net deep learning procedure as shown in FIG. 2, with T1Wpre-contrast scan as the single-channel input.

The exemplary system, method, and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can also be usedto generate high-quality human CBV maps directly from the Gd-free T1Wscans. (See e.g., FIGS. 9A and 9B). For example, FIG. 9A shows a set ofexemplary images of human brain MRI scans for T1W Gd-free pre-contrastscan 905, T1W post-contrast scan with steady-state Gd uptake 910, CBVground truth generated as the normalized ΔR1 map 915, and DeepCBVestimated directly from the pre-contrast T1W scan using the exemplarydeep learning procedure 920.

FIG. 9B illustrates a graph illustrating a region-of-interestcorrelation analysis (e.g., for T1W 925 and DeepCBV 930), withsignificant Pearson correlation coefficient ((“P.R”)=0.802) and Spearmancorrelation coefficient ((“S.R”)=0.693), according to an exemplaryembodiment of the present disclosure. In addition, the exemplary DeepCBVcan be used to map the spatial pattern of normal cognitive agingassociated changes of CBV in the human brain. Spatial pattern of changesin CBV in normal aging can be observed based on CBV fMRI over 100carefully-screened cognitively normal individuals spanning 20-72 yearsof age. In particular, left-hemisphere inferior frontal gyms (“IFG”) canbe mostly affected, and left-hemisphere IFG regional CBV can have themost reliable age-related decrease. (See e.g., FIG. 9A, frame 905). TheIFG finding was reproduced using DeepCBV on the same cohort with 178scans been processed.

Similar spatial patterns of changes in DeepCBV and CBV have beenobserved for normal aging. (See e.g., FIG. 9C). In particular, FIG. 9Cshows a set of exemplary images of three-dimensional renderings of ratesof CBV change over time (e.g., with over 100 cognitively normalindividuals spanning 20-72 years) demonstrating that there areconsistent trends between age-associated changes in DeepCBV and CBV innormal aging. ROI correlation analysis between age-associated rates ofchange in CBV (ROIs with age regressor related p<0.05) and thecorresponding DeepCBV have strong linear and monotonic relationships(e.g., with a significant Pearson correlation coefficient R²=0.599 andSpearman correlation coefficient R²=0.923). Left-hemisphere IFG regionalCBV has the most reliable age-related decrease in normal aging (e.g.,indicated by arrows 935 in FIG. 9C).

Exemplary Discussion

Gadolinium based MRI imaging can provide a wide variety of knowledge toadvance patient care. MRI is often used to investigate a new finding, orrepeatedly used to track the evolution of the pathological process. Withfindings of Gd retention, it can be beneficial to utilize MRI imaging inorder to decrease the Gd exposure.

Gd-enhanced steady-state CBV MRI imaging (see, e.g., Reference 22) canbe used to produce in vivo nonradioactive high-resolution functionalmapping of basal brain metabolism in both mice and humans. (See, e.g.,References 10 and 11). CBV can be related to regional metabolism inhealthy and diseased brains (see, e.g., References 23 and 24) and can beuseful to study cognitive aging (see, e.g., Reference 25), Alzheimerdisease (“AD”) (see, e.g., Reference 26) and tumor. (See, e.g.,Reference 27). For AD, the disease can begin by impairing neuronalfunction in a specific sub-region of the hippocampal formation. (See,e.g., Reference 11). Of functional imaging procedures sensitive tometabolism, Gd-enhanced CBV MRI can have the highest spatial resolutionthat can most readily visualize individual hippocampal sub-regions inboth mice and humans. (See, e.g., References 8 and 28). Thus,Gd-enhanced CBV can be well-suited to detect AD-related metabolismdysfunctions. (See, e.g., References 11 and 28). For brain tumorstudies, for example, GBM is one of the most common and aggressive typesof malignant brain tumors. As the most vascularized tumor in humans(see, e.g., Reference 29), its growth is closely associated with theformation of new vessels and signs of blood-brain-barrier (“BBB”)leakage. (See, e.g., Reference 30). Studies demonstrated that increasedCBV of GBM was driven by hyperactive angiogenesis (see, e.g., Reference31) and aggressive BBB leakage. (See, e.g., Reference 32). Gd-enhancedCBV is well-suited to detect GBM-related regional hyperactiveangiogenesis and BBB leakage and has become a potential imagingbiomarker for GBM detection and grading. (See, e.g., References 33-36).

Comparing the quantitative evaluation results, the exemplary DeepLearning methods have significant improvement over the low-dosage (e.g.,and the pre-contrast scans). Low MSE, high SSIM and high correlationsbetween the DeepCBV and the CBV ground truth indicate that the exemplaryDeep Learning method does not lead to significant quality degradation.High performance of GBM segmentation reflected by high Dice coefficientand low Hausdorff distance between the DeepCBV- and CBV groundtruth-derived tumor masks indicate that the estimated DeepCBV canproduce similar GBM enhancement as the full-dose scans. Reducing, oreven removing, the Gd contrast while retaining diagnostic informationcould can a large impact on patient well-being and imaging costs.

The exemplary system, method, and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can also train a3D network, taking upsampled isotropic 3D volumes as its input.Additionally, the exemplary system, method, and computer-accessiblemedium can be sensitive to scan orientation and anatomy variance; thus,scans can be acquired with the same geometry and orientation. Further,the spatial variance can be further reduced by image co-registration.

For the exemplary GBM mouse model, all MRI scans were acquired 10 daysafter the cell injection at the right-side stratum. The lack oflongitudinal dataset and GBM locations can lead to biased DeepCBVpredictions when the GBMs can be in different stages, or the GBM cellswere injected at different brain regions. To improve the robustness andaccuracy of the DeepCBV for the GBM enhancement, dataset can be enrichedby adding mice scans with GBMs at various stages and injection sites.

MSE can be chosen as the cost function to train the exemplary DeepLearning networks. The training strategy can be further improved byadding other loss functions. (See, e.g., Reference 37). In addition,Generative Adversarial Network (“GAN”) can be used, which has been shownto have outstanding performance in recovering high-frequency details ofimage reconstruction. (See, e.g., Reference 38).

The exemplary system, method, and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can apply deeplearning to generate contrast-enhanced CBV maps with reduced Gd dosage.The exemplary deep learning procedure can reduce Gd dosage by at least5-fold while preserving high-quality CBV contrast. In addition, CBV mapsof the whole brain can be directly generated from Gd-free T2W anatomicalscans, which can provide a significant benefit to human MM. Theexemplary system, method, and computer-accessible medium according to anexemplary embodiment of the present disclosure was tested on aGd-enhanced human CBV dataset. High-quality CBV maps were generateddirectly from the Gd-free T1W scans. Thus, the exemplary system, method,and computer-accessible medium, according to an exemplary embodiment ofthe present disclosure, can be used to generate high quality Gd contrastin brain MM from widely available exogenous-contrast-free structural MRIwhile preventing Gd retention.

FIG. 10 illustrates an exemplary flow diagram of an exemplary method1000 for generating a gadolinium enhanced map of a portion of a patient.For example, at procedure 1005, MRI information of the portion can bereceived. At procedure 1010, the Gd enhanced map can be generated basedon the MRI information using a machine learning procedure. At procedure1015, a Gd contrast can be generated in the Gd enhanced map using aT2-weighted MRI image of the portion.

Exemplary Magnetic Resonance Imaging Using Deepcontrast

Deep learning can produce Gd contrast in brain MRI directly from singlenon-contrast structural MRI. This was analyzed in mice. The residualattention U-Net architecture was used in the exemplary deep learningmodel to estimate Gd contrast from non-contrast T2W MRI for CBV mapping.The exemplary procedure was evaluated for both WT mice and mice with GBMat 9.4T.

Exemplary Material and Methods

T1W human brain MRI scans are acquired using an exemplary protocol (see,e.g., References 29-31), before (e.g., Pre) and 4 minutes after (e.g.,Post) intravenous bolus injection of Gadodiamide. Within each Pre-Postpair, the two scans share the same intensity scales. Scans can bebrain-extracted and spatially co-registered as described previously.(See, e.g., Reference 29-31). Intensity normalization can be performedby mapping the Pre scans to the range of [0, 1] and propagating thescaling to the post scans. CBV, a metabolic mapping utilizing GBCAcontrast, can be calculated as the difference between Post and Pre foreach pair. FIGS. 15A and 15B demonstrate the pipeline. A deep learningmodel with a Residual Attention U-Net architecture, as shown in FIG. 16,can be used to predict the GBCA contrast directly from the Pre scans. Onthe Pre and CBV scans of 600 subjects, a train-validation split can beperformed at a ratio of 6:1, while 180 subjects can be left for the testset. Evaluation of the DeepContrast model comes in two aspects. In thefirst exemplary aspect, it can be used to generate GBCA contrastpredictions on the 180-scan test set and the resulting mappings can bequantitatively compared to the ground truth CBV maps. In the secondexemplary aspect, the exemplary DeepContrast was evacuated to map theage-related CBV changes over the whole cortical mantle. To achieve this,the DeepContrast model can be applied on a previously unseen datasetthat consists of 178 T1 W Pre scans where the subject population isshown in FIGS. 18A and 18B. The T1 W Pre, CBV, and DeepContrastpredictions can be individually used to each generate an age-relatedregression t-map over 72 cortical ROIs defined by FreeSurferllparcellation. The t-map can be constructed by running a single-variablelinear regression y-x, where the dependent variable y can be the meanintensity of the ROI in each scan divided by the mean intensity of thetop 10% brightest values in the white matter 9 of that scan, while theindependent variable x can be the age of the subject. The regressiont-value for each ROI can be filled back to its spatial location to formthe t-map. Significant negative values in the t-map indicate the brainregions with decline in metabolic activities as humans get older.

Exemplary Animal Subject

Mice used in the exemplary study were divided into two groups: WT miceand mice with GBM. The WT group contained 49 healthy adult C576J/BL malemice scanned at 12-14 months old. The GBM group contained 10 adultC576J/BL male mice that were injected with PDGFB (+/+) PTEN (−/−) p53(−/−) GBM cells. (See, e.g., Reference 52). 50,000 cells in 1 μL werestereotactically injected into the brain. MRI scans of GBM mice wereobtained 10 days after injection.

Exemplary MRI Acquisition and Preprocessing

For each mouse, T2W MRI scans were acquired using the 2D T2-weightedTurbo RARE sequence at 9.4T (e.g., TR/TE=3500/45, RARE factor=8, 76 μmin-plane resolution, 450 μm slice thickness; Bruker Biospec 94/30 USRequipped with CryoProbe).

To derive the steady-state CBV maps, whole brain T2W MRI scans before(e.g., Pre) and 35 minutes after (e.g., Post) IP injection ofGadodiamide at 10 mmol/kg can be acquired with identical scanparameters. (See e.g., FIG. 11A). (See, e.g., Reference 53). As a directapproach to reduce Gd dose, the 20% low-dose CBV maps were derived, asillustrated in FIG. 11B. For scans of tumor subjects, tumor masks can begenerated in addition to the brain masks using the Fuzzy-C-Meanssegmentation. (See, e.g., Reference 54).

Exemplary Deep Learning Model Exemplary Model Architecture

The deep learning architecture utilized in the exemplary study can bethe out-stand five-layer ResAttU-Net as illustrated in FIG. 12. Thisconsists of a contraction path that encodes high-resolution data intolow-resolution representations and an expansion path that decodes suchencoded representations back to high-resolution images. The exemplarydeep learning model was implemented using PyTorch framework with CUDA10.0, 2 NVIDIA RTX 2080-TI GPUs and CentOS 6.

Both the encoder and decoder parts can be based on the U-Net structure(see, e.g., Reference 55) where each stage consists of two series of 3×32D convolutions, batch normalization, and ReLU. In the encoder part,each stage can be followed by 2×2 max-pooling for down sampling, whilefor the decoder part, four 2×2 upsampling layers convert low-resolutionrepresentation back to high resolution. Additionally, every stage of thedecoder has concatenation from the encoder at the same level to give themodel more accurate local information for assembling a preciseprediction.

To further refine the exemplary network, as illustrated in FIG. 12, theresidual blocks and the attention gates can be incorporated into theU-Net architecture. The residual block can optimize the signal andgradient propagation within a network while preventing overfitting frombeing a problem. (See, e.g., Reference 56). While the attention gate cansignificantly suppress feature responses in irrelevant backgroundregions without needing to crop an ROI. (See, e.g., Reference 57).

Exemplary Training the Model

ADAM was used as the optimizer with a learning rate of 10-3 and maximum200 epochs with early stop. A batch size of 3 and randomizenon-overlapping input images were used for training. Scans of 6standalone mice were used for validation.

Exemplary Applying the Model

The ResAttU-Net was applied to derive the steady-state CBV maps in WTmice directly from their non-contrast pre-scans. In addition todemonstrating the ability to produce CBV in the normal brain tissue, itsutility can further be used in the enhancement of pathology visibilityand delineation of brain lesions as the exemplary second objective. 49WT mice can be used in the study, with a randomized 37-6-6train-validation-test split. 6 GBM mice scans were randomly to thetraining set and the model was retrained. The performance ofDeepContrast on tumor enhancement was tested on 4 GBM mice scans.

Exemplary Evaluation and Statistical Analysis

To evaluate the performance of DeepContrast, a PSNR was used to assessthe estimation error at the voxel level. Given the limitation of PSNR oncapturing the perceptually relevant differences, the SSIM was determinedto evaluate the accuracy of estimating a processed image on thestructural level. (See, e.g., Reference 58). In addition, Pearson andSpearman correlation analysis were constructed to assess the linear andmonotonic associations between the CBV ground truth and DeepContrastrespectively. For the GBM study, besides the voxel level comparison, theDice similarity coefficient and Hausdorff distance were used to comparethe performance of DeepContrast and the CBV ground truth in tumorsegmentation.

Exemplary Results Exemplary Performance of DeepContrast in Normal BrainCBV Mapping

Example of the DeepContrast prediction and the quantitative metrics ofthe standalone 6 testing subjects are shown in FIGS. 13A and 13B.DeepContrast captured the high contrast and fine details of smallvessels with high similarity to the steady-state CBV ground truth in thenormal brain tissue. FIG. 13A shows the CBV ground truth, predictedresult, 20% low Gd dose CBV and non-contrast Pre-image of the samemouse. The Pre-image alone can provide promising prediction results thatshow strong enhancement compared to the 20% low-dose CBV that can beconsistent with the steady-state CBV maps. FIG. 13B shows thequantitative comparison of the DeepContrast and 20% Gd CBV map.DeepContrast clearly improved the CBV contrast derived from 20% Gdenhancement with a gain of 9.7% SSIM (e.g., p<0.001), an increase of 5.3dB in PSNR (e.g., p<0.001), an improvement of 14.9% increase of P.R(e.g., Pearson Correlation, p<0.05) and an 8.6% increase in S.R (e.g.,Spearman Correlation, p<0.05).

In the first exemplary aspect, the quantitative voxel-level analysis(see e.g., FIGS. 17A and 17B) yields a PSNR=29.31, Pearson R=0.808,Spearman R=0.601, SSIM=0.871, and KL divergence=0.180. This assessmentdemonstrates that even though the structural T1W Pre scans may not belike the CBV maps, DeepContrast can extract the metabolic informationfrom them and resemble CBV. In the second aspect, DeepContrast can beapplied to examine imaging correlates of cognitive aging. FIG. 19A showsthat the spatial distribution of age-related metabolism changes seen inDeepContrast predictions can be consistent to those in the CBV groundtruth. IFG and superior temporal gyms (“STG”) show the most reliableaging-induced hypometabolism (e.g., indicated by the red arrows), whileentorhinal cortex experiences the least metabolic degradation (e.g.,indicated by the green arrow). These regions identified agree withexisting findings (see, e.g., References 77-79). FIG. 19B breaks downthe t-maps into a scatter plot with each point representing a corticalROI, and it shows significant linear and monotonic correlation betweenDeepContrast prediction and CBV despite no correlation between T1W Preand CBV. FIG. 19C shows an exemplary illustration of an exemplaryreceiver operator characteristic (“ROC”) when treating the t-valueconcordance as a series of binary classification problems with 1000different binarizing thresholds. The t-values from the 72 cortical ROIscan be linearly mapped to [0, 1] respectively for CBV, T1 WandDeepContrast predictions, and can be used to indicate the regionalt-value concordances. It can be inferred that the t-values inDeepContrast predictions have significant predictive power on its CBVcounterpart, while those in T1W scans do not.

Exemplary Performance of DeepContrast in GBM CBV Enhancement

To experiment and evaluate the utility of the exemplary DeepContrastmethod in the enhancement of pathology visibility and delineation ofbrain lesions, the same ResAttU-Net architecture was trained withadditional MRI scans of the GBM mouse model.

FIGS. 14A and 14B show exemplary illustrations indicating that theDeepContrast predicted results perform significantly better than the 20%Gd CBV in both visual assessment and the quantitative evaluation. FIG.14A shows the predicted result of one slice of the same tumor subject.Compared with 20% Gd CBV, DeepContrast generated from the T2W Pre scanshows similar contrast level to the ground truth. The consistency can beobserved in the fine structure and significant contrast enhancement inboth normal tissue and the tumor region. FIG. 14B is the detailedquantitative metrics evaluation of 4 randomly selected testing tumorsubjects. Compared with the 20% Gd CBV, DeepContrast has a 4.8% increasein SSIM (e.g., P<0.05), a 2.0 dB increase in PSNR (e.g., P<0.05), and a19.6% increase in P.R (e.g., Pearson Correlation, P<0.05) and a 2.6%increase in S.R (e.g., Spearman Correlation, P<0.05). FIGS. 14A-14D showexemplary illustrations that the DeepContrast predicted results performsignificantly better than the 20% Gd CBV in both visual assessment andthe quantitative evaluation.

FIGS. 14C and 14D show further illustrations that further confirm thatthe DeepContrast models with the input of Pre only image significantlyoutperformed the 20% Gd for the tumor region. FIG. 14C shows the 3Drendering results of CBV ground truth vs DeepContrast in the FCMsegmented tumor region of a single tumor subject. Compared to low doseCBV, both the two DeepContrast models derived the tumor region moreprecisely with a similar contrast level to the ground truth.

FIG. 14D shows illustrations of exemplary quantitative comparisonbetween the DeepContrast predictions and the 20% Gd CBV of the tumorregion. The DeepContrast performed significantly better than the 20% Gdof the tumor region, with 47.0% Dice coefficient increases (e.g.,P<0.05) and 5.5 pixels Hausdorff distance reductions (e.g., P<0.05).

Exemplary Discussion and Conclusions

Findings of Gd retention may necessitate efforts to develop novelapproaches to MRI that can decrease or even eliminate Gd exposure. Inthe past, there have been several attempts to develop non-contrast MRIsequences such as ASL, TOF, and VASO. (See, e.g., Reference 59). Whilesuch sequences have been successful and applied clinically, Gd basedimaging still offers an unparalleled level of information in comparison.For example, Gd can be widely used for steady-state CBV fMRI imaging tomap basal brain metabolism in both mice and humans. (See, e.g.,References 45 and 47). CBV has been proven to be tightly coupled toregional metabolism in healthy and diseased brains (see, e.g.,References 60 and 61) and can be useful in studying cognitive aging(see, e.g., Reference 60), AD (see, e.g., Reference 61) and tumors.(See, e.g., Reference 62). Gd-enhanced CBV can also be well suited todetect GBM-related regional hyperactive angiogenesis and BBB leakage,which has become a potential imaging biomarker for GBM detection andgrading. (See, e.g., References 63-66).

Exemplary results from the exemplary study demonstrate that the GBCAcontrast mappings predicted by the exemplary DeepContrast model not onlyqualitatively and quantitatively resemble the ground truth CBV, but alsotruly contain equivalent information that can be used to generateinsights that concur with existing findings. The exemplary the exemplarymodel can generate high-quality and clinically relevant contrastmappings in the human brain from nothing more than the T1 W structuralMRI scans, the single most prevalent modality in MRI.

Removing the Gd contrast entirely while retaining diagnostic informationcould have a large impact on both patient well-being and reduction ofimaging time and costs. Recently, 3D Bayesian U-Net was applied to adataset from patients with brain tumors and healthy subjects to predictcontrast enhancement from a comprehensive multiparametric MRI protocolincluding T1w, T2w, T2w fluid-attenuated inversion recovery,diffusion-weighted imaging, and susceptibility-weighted imaging, allacquired without any Gd injections. (See, e.g., Reference 51). Thisexemplary method was limited in its inability to predict presence ofsmall vessels and an inevitably long scan time likely to exhaustpatients due to utilization of 10 multiparametric MRI scans as itsinput. In view of these limitations, DeepContrast was developed to,e.g., rely primarily, or solely, on information extracted from the mostcommonly acquired structural MRI scans. Compared to the 3D BayesianU-Net, the exemplary approach provides several improvements topredicting contrast enhancement. First, it was shown that Gd contrast inbrain MRI can be directly derived from a single non-contrast T2W MRI inboth normal brain tissue and brain lesion. High PSN, high SSIM and highcorrelations between the DeepContrast and the Gd-enhanced ground truthsuggest that the exemplary proposed method does not cause significantquality degradation. High accuracy of GBM segmentations suggestDeepContrast can detect GBM with performance like full-dose Gd-enhancedscans. Furthermore, the exemplary deep learning method can be based on ahybrid deep residual attention-aware network and can be the firstnetwork to use an attention residual mechanism to process brain MRIscans. The basic architecture of the proposed network can be a 2D U-Netthat extracts contextual information combining low-level feature mapswith high-level ones. Attention modules can be stacked such that theattention-aware features change adaptively in the deeper layers of thenetwork. This can be performed based on residual learning. The exemplaryprocedure can produce CBV mapping of small vessels with high fidelity.

To improve the robustness and accuracy of the exemplary DeepContrast forGBM enhancement, the current training dataset can be enhanced by addingMRI scans of mice injected with GBM cells at various stages anddifferent locations. Beyond diversifying the dataset, to deal with theintensity difference within and across subjects, recent advancements inintensity standardization/normalization can enhance the estimation ofthe DeepContrast enhancement when translating the exemplary model tohuman data. (See, e.g., Reference 67). MSE was chosen as the costfunction in the exemplary current model. However, the training strategycan be further improved by adding other evaluation parameters to theloss function.

The exemplary procedure according to exemplary embodiments of thepresent disclosure can be used to generate Gd contrast in brain MRIdirectly from T2W MRI scans with complete omission of GBCAs.DeepContrast can be used to provide benefits to patient care and thehealthcare system through reduction of Gd exposure, scan time, and cost.

An exemplary deep learning model according to exemplary embodiments ofthe present disclosure can be used to extract gadolinium-equivalentinformation from a single and commonly-acquired T1-weighted MRI scan,e.g., by training and optimizing the model using a unique gadolinium MRIdataset. Previous deep learning studies relied on gadolinium datasetsgenerated for radiological purposes, where post-gadolinium scans arerescaled, easing a radiologist's ability to detect and characterizebrain lesions. This rescaling, however, dramatically increasesintrasubject variability across a dataset. With the exemplary specificinterest in mapping functional brain lesions that localize to specificregions of the hippocampal formation, over the last couple of decadesgadolinium has been used to generate quantitative, high-resolution, CBVmaps. (See, e.g., References 84-86 and 94-978). These quantitative mapsdo not use any rescaling, and thus, while not the original intent, thereduced intrasubject variability in this large-scale data can bewell-suited for deep learning purposes. In parallel to generating alarge-scale and quantitative gadolinium dataset in people, a similar MRIdataset was generated in mice. Here again, the original intent was tovalidate patterns of hippocampal dysfunction observed across diseasestates, but because in mice studies subjects are siblings with identicalgenetic backgrounds, this mouse dataset can be notable for even lessinter-subject variability than in people.

Thus, e.g., this distinct cross-species and quantitative gadoliniumdataset was utilized. Beginning with mice, an exemplary deep learningmodel was designed, optimized, and trained, and then validated that itcan substitute gadolinium. This exemplary procedure was then applied topeople, by showing that trained deep learning models can visualizefunctional lesions that occur in the hippocampal formation in aging,schizophrenia, and Alzheimer's disease, and enhance structural lesioncauses by tumors. The deep learning model can be referred to as‘DeepContrast’.

Exemplary Results Exemplary DeepContrast in the Mouse Brain

The exemplary model was designed, optimized and trained the model on 49WT mice brain scans (e.g., 37 for training and 6 for validation; seemethods), in which quantitative T2-weighted gadolinium-uptake brain mapswere previously generated, and then tested on a standalone group of 6mice. Compared to gadolinium-uptake maps (“Gd-Uptake”), the DeepContrastmodel was able to derive gadolinium-predicted maps (“Gd-Predicted”) thatgenerated contrast-equivalent information across the brain, as indicatedby visual inspection and by quantitative voxel-level analyses. (Seee.g., FIGS. 21A-21F).

Next, the same network architecture was trained after adding brain MRIscans from 6 mice with GBM into the training set. Again, theDeepContrast model was able to derive gadolinium-predicted maps thatgenerated contrast-equivalent information for enhancing tumor detection,as indicated by visual inspection, quantitative voxel-level analyses,and high-enhancement region ROC analyses. (See e.g., FIGS. 21A-21F).

Exemplary DeepContrast in the Human Brain

After certain exemplary modifications to the network architecture,hyper-parameters, and training strategies, the exemplary DeepContrastmodel was adapted to be used on a human brain MRI dataset to predictgadolinium contrast from structural T1-weighted non-contrast scans. Onthe stand-alone test set with 179 scans, the gadolinium-predicted mapsresembled the ground truth gadolinium-uptake maps to a great extent,achieving the highest metrics reported by far in the literature. (Seee.g., FIGS. 22A and 22B). A follow-up correlation analysis as shown inFIG. 31 demonstrated that the similarity between the predicted contrastand the ground truth can withstand scrutiny at a finer ROI scale.

The reproducibility of the exemplary DeepContrast model was evaluated intest-retest acquisitions. While the experimentally acquiredgadolinium-uptake maps of the same subject showed high similarity, thegadolinium-predicted maps demonstrated even lower between-sessionvariation, indicating a significant test-retest reliability. (See e.g.,FIG. 22C).

Exemplary DeepContrast Visualizes Functional Lesions

Gadolinium-predicted maps were generated from non-contrast T1-weightedMRI scans with DeepContrast, and subsequently quantified CBV-predictedmaps with sub-millimeter in-plane resolution of 0.68×0.68 mm in thecoronal planes and slice thickness of 3 mm. Then, voxel-based analyses(“VBA”) and region-based studies were performed to localize the regionsin such CBV-predicted maps that can be reliably affected by normalaging, by Schizophrenia clinical high-risk (“CHR”) and by AD.

Exemplary Normal Aging determination. The first study aimed to validatewhether DeepContrast can capture the subtle aging effects on basalmetabolism across the hippocampal circuit. The D) was found to be theregion most reliably affected by aging within the hippocampal formation,as indicated in FIG. 23B. Further investigations demonstrated that theCBV-predicted maps carried similar age-related changes as observed inground truth CBV maps, both within selected landmark regions known to betargeted by aging (see e.g., FIGS. 32A-32C) and over all cortical ROIsin general. (See e.g., FIGS. 33A-33C).

Exemplary Schizophrenia determination. The second study aimed tovalidate whether DeepContrast can capture the regional vulnerability inpatients who can be clinically high risk for Schizophrenia. CA1 wasfound to be the region most reliably affected within the hippocampalformation despite that, the cluster-level significance did not reach thethreshold of 0.05, as shown in FIG. 23C. Follow-up slice-based analysisin the left and right CA1 regions indicated a significantpatient-related increase in five consecutive slices (e.g.,thickness=0.68 mm) within the left anterior CA1 region.

Exemplary Alzheimer's Disease determination. The third study aimed tovalidate whether DeepContrast can capture the regional vulnerability inpatients with Alzheimer's Disease dementia. The right transentorhinalcortex was found to be the region most reliably affected in patientswithin the hippocampal formation, as indicated in FIG. 23D.

Exemplary DeepContrast Enhances Structural Lesions

Exemplary Brain Tumor determination. In order to accurately capture thehigh variance present within brain tumors, the exemplary DeepContrastmodel was trained with a large-scale brain tumor MM dataset, which againwas able to generate contrast predictions that were similar to theground truth. (See e.g., FIGS. 24A-24C). While the overall performanceover the entire brain region can be inferior to the healthy human brainmodel, the PSNR can still be quite remarkable. Further, the qualitativeenhancement of the tumor region can be clearly seen in thegadolinium-predicted map, both in 2D slices and in 3D volume renderings.Moreover, the high-enhancement regions in the gadolinium-predicted mapshad considerable overlap with those in the gadolinium-uptake maps, asshown in the ROC study. (See e.g., FIG. 2C).

Exemplary Breast Tumor determination. The exemplary DeepContrast modelwas evaluated on other organs. A study using the Breast-MRI-NACT-Pilotimage collection, which contains longitudinal dynamic contrast, enhanced(“DCE”) MRI studies on patients undergoing neoadjuvant chemotherapy(“NACT”) for invasive breast cancer was conducted. As breast tumors canbe hard to distinguish from healthy tissue without the additionalcontrast provided by GBCAs, the role of DeepContrast can be especiallycritical in screening for breast cancer. Similar to the case in braintumors, the qualitative and quantitative evaluation results shown inFIGS. 24D-24F indicated the promising potential of applying DeepContrastto breast tumor enhancement.

Exemplary Discussion

By using a quantitative gadolinium dataset in mice and people, theexemplary hypothesis that deep learning can, for example, generategadolinium-equivalent information from a single and common MRI scanacross an array of lesions was evaluated.

Gadolinium's utility for MRI can be organized around two distinctpathophysiology. The first pathophysiology can be a breakdown of theblood-brain barrier that often accompanies many structural lesions, andin which case gadolinium extravasates into the parenchyma and enhanceslesion detection. The second pathophysiology can be alterations inneuronal metabolism, typifying most functional disorders, in which caseintravascular gadolinium can be used to quantify regional CBV, ahemodynamic variable tightly coupled to energy metabolism. Individualmodels were optimized for each of the 5 disorders investigated.Nevertheless, as gadolinium's utility can be reduced to twopathophysiologies.

Gadolinium contrast can be much subtler for functional compared tostructural lesions, and the exemplary findings in aging, schizophrenia,and Alzheimer's disease can be considered the strongest validation.Nevertheless, since most of the concerns over gadolinium's safety haveemerged when cancer patients can be imaged multiple times over thecourse of their disease, validating DeepContrast in tumors was equallyimportant.

DeepContrast's utility can be organized according to its two broadapplications. The first exemplary application can be for research. Thereare now over two dozen brain MRI databases, such as ADNI (see e.g., FIG.35), whose sole purpose can be to advance clinical research. StandardT1-weighted MRI scans can be the most common acquisition across all ofthese datasets, typically acquired for mapping regional structuraldifferences, such as regional volume or cortical thickness. DeepContrastcan be retroactively applied to these datasets, and so investigators cannow generate functional maps, significantly expanding pathophysiologicalinsight that can be derived across the range of disorders.

DeepContrast's second application can be provided for patient care. Forpatient populations with structural lesions, such as cancer patients forexample, gadolinium can always be considered the gold standard,particularly during initial evaluation or for surgical planning. Forthese patients, however, DeepContrast may substitute gadolinium whentracking the course of the disease or treatment response. For patientpopulations with functional lesions, those with neuropsychiatric andneurodegenerative disorders, a T1-weighted scan can be ordered as partof standard clinical practice, to rule-out structural lesions. For thesepatients, deriving CBV maps via DeepContrast potentially obviates theneed for ordering other more invasive, burdensome, and expensiveneuroimaging studies for mapping metabolic dysfunction.

Exemplary Methods Exemplary Subjects

Healthy Mouse Brain. 49 healthy adult C576J/BL male mice (e.g., 12-14months old) were used.

Exemplary Mouse GBM 9 adult C576J/BL male mice were included, which wereinjected with PDGFB˜(+/+) PTEN˜(−/−) p53˜(−/−) glioblastoma cells. (See,e.g., References 98). 50,000 cells in 1 μL solution werestereotactically injected into the brain. MRI scans of GBM mice wereobtained 10 days after injection.

Exemplary Healthy Human Brain and Human Aging determination. The healthyhuman MRI data was aggregated from a collection of previous acquisitionsat Columbia University, resulting in 598 subjects (e.g., 16-94 yearsold) with single acquisitions, and another 11 subjects with baseline andfollow-up acquisitions 14 days apart. The aging study consists of 177subjects (e.g., 20-72 years old) that can be cognitively normal.

Exemplary Human CHR determination. Scans from a previous study thatincluded 92 subjects (e.g., 15-35 years old), among which 74 areschizophrenia clinical high-risk patients, and 18 are normal controls,were collected.

Exemplary Human AD determination. 50 CN and 50 AD subjects were randomlysampled from the Alzheimer's Disease Neuroimaging Institute (“ADNI”),resulting in a 100-subject (e.g., 60-90 years old) dataset.

Exemplary Human GBM determination. 268 subjects (e.g., 36-86 years old)were selected from the original 335 subjects present within the BrainTumor Segmentation (“BraTS”) dataset, based on successful segmentationthrough the MALPEM segmentation pipeline. (See, e.g., Reference 99).

Exemplary Human Breast Cancer. All 68 subjects from the The CancerImaging Archive (“TCIA”) Breast MRI NACT Pilot dataset were used.

Exemplary Image Acquisition Protocols

Exemplary Healthy Mouse Brain and Mouse GBM determination. CBV-fMRI wasused to image two independent groups of mice, young and old male WT andGBM mice used in healthy mouse brain and mouse GBM studies, with theimaging protocol as previously described. (See, e.g., Reference 100). ABruker BioSpec 94/30 (e.g., field strength, 9.4 T; bore size, 30 cm)horizontal small animal MRI scanner equipped with CryoProbe and softwareParaVision 6.0.1 (e.g., Bruker BioSpin, Billerica, Mass., USA) and a23-mm 1H circularly polarized transmit/receive capable mouse head volumecoil were used for the imaging. Mice were anesthetized using medical airand isoflurane (e.g., 3% volume for induction, 1.1-1.5% for maintenanceat 1 liter/min air flow, via a nose cone). A flowing water heating padwas used to maintain the body temperature at around 37° C. Sterile eyelubricant was applied before each scan. T2-weighted images were acquiredbefore and 36 min after intraperitoneal injections of thegadolinium-based contrast agent Gadodiamide (e.g., Omniscan; GEHealthcare, Princeton, N.J., USA) at the dosage of 10 mmol/kg.T2-weighted images were acquired with a Refocused Echoes sequence (e.g.,repetition time (“TR”)=3,500 ms, effective echo time (“TE”)=45 ms, rapidacquisition and RARE factor=8, voxel size=76×76×450 μm).

Exemplary Healthy Human Brain and Human Aging determination. The imageswere acquired under a steady-state CBV-fMRI protocol as previouslydescribed. (See, e.g., Reference 95). A gradient echo T1-weighted scan(e.g., TR=6.7 ms, TE=3.1 ms, FOV=240×240×192 mm, voxel size=0.9×0.9×0.9mm) was acquired before a pair of un-scaled T1-weighted images (e.g.,TR=7 ms, TE=3 ms, FOV=240×240×196 mm, voxel size=0.68×0.68×3 mm), allusing a Philips Achieva 3.0-T MRI scanner. The image resolution usedresults from a systematic exploration of the scan protocol's parameters.Scans were acquired before and after a bolus injection of aGadolinium-based contrast agent (e.g., Omniscan, GE Healthcare).

Exemplary Human CHR determination. The T1-weighted images were acquiredusing the same scan parameters as mentioned in the studies above (e.g.,Philips Achieva 3.0-T MRI scanner, TR=7 ms, TE=3 ms, FOV=240×240×196 mm,voxel size=0.68×0.68×3 mm).

Exemplary Human ADNI determination. The images included in the exemplarystudies were acquired using a customized back-to-back 3D magnetizationprepared rapid gradient echo (e.g., MP-RAGE) protocol, yieldingnear-isotropic images (e.g., voxel size around 1×1×1 mm). (See, e.g.,Reference 101).

Exemplary Human GBM determination. The images were acquired usingdifferent protocols from 19 institutions, the majority of which wasacquired with 3D acquisition and voxel spacing of isotropic 1 mm. (See,e.g., Reference 102).

Exemplary Human Breast Cancer TCIA determination. All breast MRI used inthis study were acquired on a 1.5-T scanner (e.g., Signa, GE Healthcare,Milwaukee, Wis.) using a bilateral phased array breast coil. The MRimaging protocol included a 3D localizer and a unilateral sagittal DCEacquisition. The DCE acquisition utilized a high spatial resolution, lowtemporal resolution, T1-weighted, fat-suppressed 3D fastgradient-recalled echo sequence developed for pre-surgical staging(e.g., TR=8, TE=4.2, flip angle=20 degrees; FOV=18-20 cm, acquisitionmatrix=256×192×60, voxel size=0.7×0.94×2.0 mm). A minimum of three timepoints were acquired during each contrast-enhanced MRI protocol: apre-contrast scan (t0), followed by 2 consecutive post-contrast timepoints: early (t1) and late (t2) phases. All 161 post-contrast scansused in this study were acquired at the earlier time point (t1), whichis 2.5 minutes after Gd injection. The gadopentetate dimegluminecontrast agent (e.g., Magnevist, Bayer HealthCare, Berlin, Germany), wasinjected at a dose of 0.1 mmol/kg of body weight (e.g., injectionrate=1.2 mL per second) followed by a 10 mL saline flush, with injectionstarting coincident with the start of the early t1 phase acquisition.Fat suppression was performed using a frequency-selective inversionrecovery preparatory pulse.

Exemplary Preprocessing and Partitioning

Exemplary Healthy Mouse Brain determination. In total, 49 WT mice wereused in this study. Whole brain T2W MRI scans before (T2W) and 35minutes after intraperitoneal injection (T2W-CE) of Gadodiamide at 10mmol/kg were acquired with identical scan parameters as previouslydescribed in CBV-fMRI protocol. The Gd-Uptake ground truth wasquantified with the standardized delta-R2, which was derived using thesame method as discussed before (see, e.g., Reference 100), followed bya standardization to the dynamic range of [0, 1]. 3D PCNN (see, e.g.,Reference 103) with manual correction was used to generate brain masks,which was used as training fields over which the model was optimized andperformance metrics were calculated. A train-validation-test ratio at8:1:1 was applied in the healthy mouse brain model training.

Exemplary Mouse GBM determination. For scans of tumor subjects, the CBVmaps and brain masks were derived using the same methods as descriptedin the healthy mouse brain study, and tumor masks were generated inaddition to the brain masks using the Fuzzy-C-Means segmentation. (See,e.g., Reference 104). 6 GBM subjects were added to the training setwhile 3 GBM subjects replaced the original testing set of the HealthyMouse Brain Model.

Exemplary Healthy Human Brain. T1-weighted MRI scans were acquired usingthe protocols as described previously (see, e.g., References 95 and 96),before (T1W) and 4 minutes after (T1W-CE) intravenous injection ofGadodiamide. During the MRI acquisition for the same session, thereceiver gain was kept constant and the offset was set to zero, and as aresult, the T1W and T1W-CE scans share the same scaling and zeroshifting. Each T1W & T1W-CE pair was spatially aligned when provided.For intensity normalization, each T1W scan was compressed to the dynamicrange of [0, 1], and the corresponding T1W-CE scan was scaledaccordingly to match the constant scaling. The Gd-Uptake ground truthwas quantified with the steady-state MRI method (see, e.g., Reference95), by subtracting the normalized T1W scans from the respective T1W-CEscans. Brain masks were generated using FSL, which was used as trainingfields over which the model was optimized and performance metrics werecalculated. The train-validation split was completed at a 7:2 ratio,while 179 subjects were left for the test set.

Exemplary Human Aging determination. The 177-subject cohort used for theaging study was a subset of the 179 subjects in the test set of theHealthy Human Brain Model, where 2 subjects were dropped due to lowsegmentation quality through the FreeSurfer (v6.0.0) Parcellation. (See,e.g., Reference 105). After normalization to the dynamic range of [0,1], the scans were directly treated as inputs to the model to generateGd-Uptake estimations.

Exemplary Human CHR determination. The 94-scan cohort for the CHR studywas acquired using the same scan parameters as those used to train theDeepContrast Healthy Human Brain Model, which ensures minimaldiscrepancy in scan appearance. CHR patients present very littlestructural deformation, which ensures minimal discrepancy in scananatomy. Therefore, no additional measures need to be taken to deal withappearance or anatomy variances. After normalization to the dynamicrange of [0, 1], the scans were directly treated as inputs to the model.

Exemplary Human AD determination. The back-to-back repeated baselinescans for each subject in the AD study cohort were gathered, and theresulting dataset contains 100 scans of normal controls and 100 scans ofpatients with dementia. An exemplary challenge was that the appearanceand anatomy of the scans used in the AD study notably differ from thoseused to train the DeepContrast Healthy Human Brain Model. They wereacquired under the same field strength (i.e., 3T), but the specific scanparameters such as echo time and repetition time can be differentbetween the ADNI protocol and the CBV-fMRI protocol, thus yielding themismatch in appearance. The subjects in the AD study can be generallyolder (e.g., 60-90 years old) and half of them suffered fromAlzheimer's, thus resulting in the mismatch in anatomy. These issueswere approached by first minimizing the between-cohort appearancedifference using a dynamic histogram warping (“DHW”) procedure (see,e.g., Reference 106) as it was demonstrated to be among the best linearand non-linear intensity matching methods in medical imaging. (See,e.g., Reference 107). For example, the mean normalized-brain-region2048-bin histogram of each cohort were calculated, a bin-to-bin mappingbetween the cohorts were derived, and the mapping was applied to eachindividual scan in the AD study. Secondly, the anatomical difference wasminimized by running a diffeomorphic registration prior to applying theDeepContrast model. After these two procedures, the scans werenormalized to the dynamic range of [0, 1] and they were provided to themodel to generate the gadolinium-predicted maps and subsequently theCBV-predicted maps.

Exemplary Human GBM determination. The data used in the Human GBM studyis from an open dataset called BraTS. (See, e.g., References 105-108).The BraTS dataset includes T1W, T1W-CE, and tumor region scans. The T1Wand T1W-CE pairs were spatially aligned when provided. Rigidregistration was performed to register all scans across subjects to acommon template space. T1W scans were normalized using the maximum ofthe non-tumor region present within each scan. Scaling correction wasthen used to adjust T1W-CE scans. Specifically, T1W and T1W-CE pairs mayneed to be in the same scaling system in order to determine Gd-Uptake.This scaling correction can be applied by identifying a region, whichremains fairly unchanged after contrast enhancement. White matter hasshown to demonstrate this property. (See, e.g., Reference 111). Whitematter regions were identified using the MALPEM segmentation pipeline(see, e.g., Reference 99) and T1W-CE scans were then scaled using ascaling ratio calculated from the average intensities in the T1W andT1W-CE white matter regions.

Exemplary Human Breast Cancer determination. For each DCE acquisition,the non-contrast (T1W) scan and the scan acquired at the first timepoint of the DCE protocol (T1W-CE) were included, totaling a number of161 pairs. T1W and T1W-CE pairs were both normalized using the maximumof the T1W scans before being fed into the DeepContrast model.

Exemplary DeepContrast Implementations

All five model variants developed in the exemplary studies, as shown inFIG. 20, share the common residual attention U-Net (“RAU-Net”)architecture. (See e.g., FIGS. 26A-26D). Model inputs can be thenon-contrast MRI scans, while the outputs can be the correspondingpredicted gadolinium contrast. The exemplary inputs and outputs can bein equal dimensions and can be either 2D or 3D depending on the natureof the scan protocols (e.g., 2D slices can be used for 2D MRI scans,whereas 3D volumes can be used for 3D MRI scans).

The RAU-Net can include features from the U-Net architecture (see, e.g.,Reference 112), and can include residual blocks (see, e.g., Reference113) and the attention gates. (See, e.g., References 114 and 115). As anexample of a convolutional neural network (“CNN”), the U-Net extractsimaging features by utilizing local convolutions along the entire imageor volume. The U-Net consists of several encoding layers across whichthe image dimension shrinks, whereas the feature dimension increases sothat compact high-level abstractions can be generated along the process,and the same number of decoding layers to decipher these abstractionsinto image space information. The add-on residual blocks simplify theentities to be approximated across each layer and therefore enablestraining of deeper networks, while the attention gates learn todifferentially enhance or suppress specific regions in the feature mapsso that the downstream outcomes better suit the desired task.

For example, the encoding and decoding paths consist of the same numberof residual convolution blocks that utilize concatenation, attentionmechanisms and skip connections such that layers feed not only into thenext layer, but also into the layer after the next layer. On theencoding path, each residual block can be followed by a max-poolinglayer, and the last feature map feeds into a bottleneck layer with 3×3convolution and batch normalization, connecting the deepest layer to thedecoding path with 4 more blocks alternating one un-pooling layer andone residual block. Skip connections concatenate the output of eachdense layer in the encoding path with the respective un-pooled featuremap of the same size before feeding it as input to the decoding residualblock. The output of the last decoding layer can be the input for a 1×1convolution layer that produces the final Gd-Predicted map.

Exemplary Healthy and Tumor Mouse Brain Model. The exemplary model usedin mouse studies (see e.g., FIG. 27) can be a 2D RAU-Net that consistsof 5 encoding and decoding layers. The exemplary model input can be a 2Daxial slice of the mouse brain scans. Adam optimizer with a learningrate of 0.001 was used in this study. The exemplary batch size can be 3and the loss function can be MSE.

Exemplary Healthy Human Brain Model. The exemplary model used in thehealthy human study and further applied to the Aging, CHR and AD studies(see e.g., FIG. 28) can be a 2D RAU-Net that consists of 6 encoding anddecoding layers. The exemplary model input can be a 2D coronal slice ofthe human brain scans. SGD optimizer with an adaptive learning ratehandle with 0.1 initial learning rate was used in this study. Theexemplary batch size can be 4 and a robust adaptive loss function (see,e.g., Reference 116) was utilized. The robust adaptive loss function canbe a generalization of the Cauchy/Lorentzian, Geman-McClure,Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2,and L2 loss functions. By introducing robustness as a continuousparameter, the robust adaptive loss function facilitates proceduresbuilt around robust loss minimization to be generalized, which improvesperformance on basic vision tasks like calculating the intensity mappingfunction in the exemplary case.

Exemplary Tumor Human Brain Model. The exemplary model used in the humantumor brain study (see e.g., FIG. 29) can be a 3D RAU-Net that consistsof 5 encoding and decoding layers. The exemplary model input can be a 3Dhuman brain volume. SGD optimizer with an adaptive learning rate handlewith 0.001 initial learning rate was used in this study. The exemplarybatch size can be 1 and the robust adaptive loss function (see, e.g.,Reference 116) was utilized.

Exemplary Tumor Human Breast Model. The exemplary model used in thetumor human breast study (see e.g., FIG. 30) can be a 2D RAU-Net thatconsists of 6 encoding and decoding layers. The exemplary model inputcan be a 2D sagittal slice of the human breast scans. SGD optimizer withan adaptive learning rate handle with 0.001 initial learning rate wasused in this study. The exemplary batch size can be 4 and a robustadaptive loss function (see, e.g., Reference 110) was utilized.

Exemplary Statistical Methods

Estimation-GT similarity assessments (e.g., applicable to Healthy MouseBrain, Healthy Human Brain, Mouse GBM, Human GBM, Human Breast Cancer).PSNR, SSIM, P.R and S.R were used to quantify the performance of all theDeepContrast models. PSNR, Pearson correlation coefficient, and Spearmancorrelation coefficient were evaluated within the brain region, and SSIMwas calculated in the minimum bounding box around the brain.

Exemplary Tumor segmentation performance assessments (e.g., applicableto Mouse GBM, Human GBM, Human Breast Cancer). In addition to the basicsimilarity assessments, Dice coefficient and Hausdorff distance wereused in the evaluation metrics for tumor studies to evaluate the tumorsegmentation similarity based on the Gd-Predicted map and its groundtruth Gd-Uptake. For the generation of the ROC curve, the ground truthGd-uptake images were binarized. This was performed using an Otsu filter(see, e.g., Reference 117) which automatically selected the thresholdvalue dividing the voxels into 2 classes. The area under the ROC curve(“AUC”) was then created by comparing the continuous prediction to thebinarized ground truth using Scikit-learn. (See, e.g., Reference 118).

Exemplary Voxel-based analysis for regional vulnerability localization:Human Aging. Voxel-based analysis (see e.g., FIG. 23B) was performed byfirst transforming the non-contrast images using a diffeomorphicregistration procedure (see, e.g., Reference 119) with nearest-neighborinterpolation to an unbiased brain template created from the 177-scanpopulation. (See, e.g., Reference 119). The gadolinium-predicted map wasgenerated by the Healthy Human Brain model using the native-spacenon-contrast T1W scans as the input and was subsequently used toquantify CBV-predicted maps by normalizing them by their respective meanvalue among the top 10% brightest voxels within the brain region. TheseCBV-predicted maps were then transformed into that template using theidentical transformation parameters calculated from the registrationprocess, and subsequently smoothed using a 3 mm-diameter sphericalkernel. Transformed CBV-predicted maps were analyzed with a generallinear model implemented in SPM12. (See, e.g., Reference 120). Data wereanalyzed with a multiple regression model, including sex as a covariateand age as the regressor. Age-related differences were contrasted usingStudent's t test. FreeSurfer regional segmentations were then performedon the unbiased template image, and the hippocampal formation mask canbe generated by binarizing and combining the labels corresponding to thehippocampus and entorhinal cortex (“EC”). (See, e.g., Reference 105).The age-related regression t-map was then projected onto the MNI-152brain template (see, e.g., References 121-123) using diffeomorphictransformation with nearest-neighbor interpolation. The result wasthresholded at p<0.005 and corrected for multiple comparisons at thecluster level within the hippocampal formation using a Monte-Carlosimulation implemented in AFNI-3dClustSim (see, e.g., References124-126) run for 10,000 iterations to yield a corrected p<0.05. Thefinal corrected age-related regression t-map was then overlaid onto theMNI-152 template in cross-section using 3DSlicer (see, e.g., Reference127), and also displayed as composite-with-shading volume rendering oversemi-transparent models of the hippocampal formation.

Exemplary ROI-based analysis: DG in Human Aging. The 177 native-spaceCBV-predicted scans were used to conduct the DG ROI analysis. A multiplelinear regression with sex as a covariate and age as the regressor wasconducted over the bilateral DG, as defined by FreeSurfer parcellation.A scatter plot was drawn (see e.g., FIG. 23E) with each pointrepresenting the DG-mean CBV-predicted value after removal of sex effectfor one subject.

Exemplary Whole Brain Aging Analysis. The exemplary gadolinium-predictedmap was generated in the native space of each subject and was afterwardsused for CBV quantification together with the experimentally acquiredground truth Gd-Uptake using the same whole brain top 10% meannormalization. Similarly, the T1W scans were normalized to generate acomparable counterpart. The CBV (e.g., quantified from Gd-Uptake),CBV-Predicted (e.g., quantified from Gd-Predicted), and normalized T1Wscans were used for age-related regression in the multiple brainregions. Multiple linear regressions with sex as a covariate and age asthe regressor were conducted using the mean CBV/CBV-Predicted/T1W valuesextracted from the region across 177 subjects, over selected landmarks(see e.g., FIGS. 32A-32D) and over all 72 cortical ROIs. (See e.g.,FIGS. 33A-33C). The ROIs were given by FreeSurfer parcellation over theT1W scans in the native space in order to minimize segmentation errors.

Exemplary Voxel-based analysis for regional vulnerability localization:Human CHR. Voxel-based analysis (see e.g., FIG. 23C) was performed byfirst transforming the non-contrast images using a diffeomorphicregistration procedure (see, e.g., Reference 119) with nearest-neighborinterpolation to an unbiased brain template created from the 94-scanpopulation. (See, e.g., Reference 119). The gadolinium-predicted map wasgenerated by the Healthy Human Brain model using the native-spacenon-contrast T1W scans as the input, and was subsequently used toquantify CBV-predicted maps by normalizing them by their respective meanvalue among the top 10% brightest voxels within the brain region. Theseexemplary CBV-predicted maps were then transformed into that templateusing the identical transformation parameters calculated from theregistration process, and subsequently smoothed using a 3 mm-diameterspherical kernel. Transformed CBV-predicted maps were analyzed with ageneral linear model implemented in SPM12. Data were analyzed with atwo-sample t-test after controlling for global variables. CHR-relateddifferences were contrasted using Student's t test. FreeSurfer regionalsegmentations were then performed on the unbiased template image, andthe hippocampal formation mask was generated by binarizing and combiningthe labels corresponding to the hippocampus and EC. The CHR-relatedregression t-map was then projected onto the MNI-152 brain templateusing diffeomorphic transformation with nearest-neighbor interpolation.The exemplary result was thresholded at p<0.005 while multiplecomparisons at the cluster level of the two clusters (e.g., p=0.3) doesnot reach p<0.05. The final corrected CHR-related regression t-map wasthen overlaid onto the MNI-152 template in cross-section using 3DSlicer,and also displayed as composite-with-shading volume rendering oversemi-transparent models of the hippocampal formation.

Exemplary ROI-based analysis: Left anterior CA1 in Human CHR. The 94template-space CBV-predicted scans were used to conduct the leftanterior CA1 ROI analysis. A two-sample t-test was conducted over theleft anterior CA1. A box plot overlaid with individual data points wasdrawn (see e.g., FIG. 23F) to indicate the group-wise difference betweenthe normal controls and the CHR patients.

Exemplary Slice-based analysis for regional vulnerability localization:Human CHR. Slice-based analysis (see e.g., FIG. 34A-34C) was performedby first transforming the non-contrast images using a diffeomorphicregistration procedure with nearest-neighbor interpolation to anunbiased brain template created from the 94-scan population. Thegadolinium-predicted map was generated by the Healthy Human Brain modelusing the native-space non-contrast T1W scans as the input and wassubsequently used to quantify CBV-predicted maps by normalizing them bytheir respective mean value among the top 10% brightest voxels withinthe brain region. These CBV-predicted maps were then transformed intothat template using the identical transformation parameters calculatedfrom the registration process, and subsequently smoothed using a 3mm-diameter spherical kernel. Next the unbiased template as well asthese CBV-predicted scans to an isotropic resolution (e.g., voxelsize=0.68×0.68×0.68 mm) were sampled using cubic spline interpolation.The hippocampal subfields of the template were parcellated usingFreeSurfer, and further cut the left and right CA1 subregions in thehippocampus into slices along the long axes of these structures. Theslice-mean CBV-Predicted values were computed for each slice, followedby a 3-slice sliding window averaging to smooth the results. Two-samplet-tests were performed over the smoothed slice-mean CBV-Predicted valuesto generate the slice-based analysis results.

Exemplary Voxel-based analysis for regional vulnerability localization:Human AD. Voxel-based analysis (see e.g., FIG. 23D) was performed byfirst transforming the non-contrast images using a diffeomorphicregistration procedure with nearest-neighbor interpolation to anunbiased brain template created from the 200-scan (e.g., 100 subjectseach with 2 back-to-back repeated scans) population. These non-contrastscans were run through the DeepContrast Healthy Human Brain Model togenerate CBV-predicted maps, which were subsequently smoothed using a 3mm-diameter spherical kernel. Unlike in the aging study, the applicationof DeepContrast can be performed after the registration process to helpeliminate major anatomical variances, since the deformations present inthe diseased population have not been previously observed by the modeltrained on healthy data. Gd-Predicted scans, the direct output of themodel, can be used to quantify CBV-predicted maps using the same methodas described in the aging study above. These exemplary CBV-predictedmaps, already co-registered upon creation, were analyzed with a generallinear model implemented in SPM12. Data were analyzed with a multipleregression model, including age, sex and subject identity as covariatesand diagnostic class (e.g., cognitive normal vs. dementia) as theregressor. AD-related differences were contrasted using Student's ttest. FreeSurfer regional segmentations were then performed on theunbiased template image, and the hippocampal formation mask wasgenerated by binarizing and combining the labels corresponding to thehippocampus and EC, while an extended hippocampal formation mask wasadditionally generated to also include the parahippocampal cortex. TheAD-related regression t-map was then projected onto the MNI-152 braintemplate using diffeomorphic transformation with nearest-neighborinterpolation. The result was thresholded at p<0.005 and corrected formultiple comparisons at the cluster level within the extendedhippocampal formation using a Monte-Carlo simulation implemented inAFNI-3dClustSim run for 10,000 iterations to yield a corrected p<0.05.The final exemplary corrected AD-related regression t-map was thenoverlaid onto the MNI-152 template in cross-section using 3DSlicer, andalso displayed as composite-with-shading volume rendering oversemi-transparent models of the hippocampal formation.

Exemplary ROI-based analysis: Right TEC in Human AD. The exemplary 200template-space CBV-predicted scans were used to conduct the righttransentorhinal cortex (“TEC”) ROI analysis. A two-sample t-test wasconducted over the right TEC, at the boundary between the right EC andthe right parahippocampal cortex (“PHC”). The region was defined as theintersection between the EC-PHC region and a sphere centered at themiddle of the EC-PHC intersection and spanning a diameter of the extentof the EC-PHC intersection. A box plot overlaid with individual datapoints was drawn (see e.g., FIG. 23G) to indicate the group-wisedifference between the normal controls and the AD patients.

FIG. 36 shows a block diagram of an exemplary embodiment of a systemaccording to the present disclosure. For example, exemplary proceduresin accordance with the present disclosure described herein can beperformed by a processing arrangement and/or a computing arrangement(e.g., computer hardware arrangement) 805. Such processing/computingarrangement 3605 can be, for example entirely or a part of, or include,but not limited to, a computer/processor 3610 that can include, forexample one or more microprocessors, and use instructions stored on acomputer-accessible medium (e.g., RAM, ROM, hard drive, or other storagedevice).

As shown in FIG. 36, for example a computer-accessible medium 3615(e.g., as described herein above, a storage device such as a hard disk,floppy disk, memory stick, CD-ROM, RAM, ROM, etc., or a collectionthereof) can be provided (e.g., in communication with the processingarrangement 3605). The computer-accessible medium 3615 can containexecutable instructions 3620 thereon. In addition or alternatively, astorage arrangement 3625 can be provided separately from thecomputer-accessible medium 3615, which can provide the instructions tothe processing arrangement 3605 so as to configure the processingarrangement to execute certain exemplary procedures, processes, andmethods, as described herein above, for example.

Further, the exemplary processing arrangement 3605 can be provided withor include an input/output ports 3635, which can include, for example awired network, a wireless network, the internet, an intranet, a datacollection probe, a sensor, etc. As shown in FIG. 36, the exemplaryprocessing arrangement 3605 can be in communication with an exemplarydisplay arrangement 3630, which, according to certain exemplaryembodiments of the present disclosure, can be a touch-screen configuredfor inputting information to the processing arrangement in addition tooutputting information from the processing arrangement, for example.Further, the exemplary display arrangement 3630 and/or a storagearrangement 3625 can be used to display and/or store data in auser-accessible format and/or user-readable format.

The foregoing merely illustrates the principles of the disclosure.Various modifications and alterations to the described embodiments willbe apparent to those skilled in the art in view of the teachings herein.It will thus be appreciated that those skilled in the art will be ableto devise numerous systems, arrangements, and procedures which, althoughnot explicitly shown or described herein, embody the principles of thedisclosure and can be thus within the spirit and scope of thedisclosure. Various different exemplary embodiments can be used togetherwith one another, as well as interchangeably therewith, as should beunderstood by those having ordinary skill in the art. In addition,certain terms used in the present disclosure, including thespecification, drawings and claims thereof, can be used synonymously incertain instances, including, but not limited to, for example, data andinformation. It should be understood that, while these words, and/orother words that can be synonymous to one another, can be usedsynonymously herein, that there can be instances when such words can beintended to not be used synonymously. Further, to the extent that theprior art knowledge has not been explicitly incorporated by referenceherein above, it is explicitly incorporated herein in its entirety. Allpublications referenced are incorporated herein by reference in theirentireties.

EXEMPLARY REFERENCES

The following references are hereby incorporated by reference in theirentireties.

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What is claimed is:
 1. A non-transitory computer-accessible medium having stored thereon computer-executable instructions for generating at least one gadolinium (Gd) enhanced map of at least one portion of at least one patient, wherein, when a computing arrangement executes the instructions, the computing arrangement is configured to perform procedures comprising: receiving magnetic resonance imaging (MM) information of the at least one portion; and generating the at least one Gd enhanced map based on the MRI information using at least one machine learning procedure.
 2. The computer-accessible medium of claim 1, wherein the at least one Gd enhanced map is a full dosage Gd enhanced map.
 3. The computer-accessible medium of claim 2, wherein the at least one full dosage Gd enhanced map is at least one full dosage Gd enhanced cerebral blood volume map.
 4. The computer-accessible medium of claim 1, wherein the at least one machine learning procedure is a convolutional neural network.
 5. The computer-accessible medium of claim 1, wherein the MRI information includes (i) at least one low-dosage Gd MM scan, or (ii) at least one Gd-free MM scan.
 6. The computer-accessible medium of claim 1, wherein the computer arrangement is further configured to generate a Gd contrast in the at least one Gd enhanced map using a T2-weighted MRI image of the at least one portion.
 7. The computer-accessible medium of claim 1, wherein the at least one machine learning procedure includes at least one attention unit and at least one residual unit.
 8. The computer-accessible medium of claim 7, wherein the at least one machine learning procedure includes at least five layers.
 9. The computer-accessible medium of claim 8, wherein the at least one machine learning procedure includes at least one contraction path configured to encode at least one high resolution image into at least one low resolution representation.
 10. The computer-accessible medium of claim 9, wherein the at least one machine learning procedure includes at least one expansion path configured to decode the at least one low resolution representation into at least one further high-resolution image.
 11. The computer-accessible medium of claim 1, wherein the at least one machine learning procedure includes at least five encoding layers and at least five decoding layers.
 12. The computer-accessible medium of claim 11, wherein each of the at least five encoding layers and each of the at least five decoding layers includes a residual connection.
 13. The computer-accessible medium of claim 11, wherein each of the at least five encoding layers and each of the at least five decoding layers include two series of 3×3 two-dimensional convolutions.
 14. The computer-accessible medium of claim 13, wherein (i) each of the at least five encoding layers is followed by a 2×2 max-pooling layer, and (ii) each of the at least five decoding layers is followed by at least one 2×2 upsampling layers.
 15. The computer-accessible medium of claim 1, wherein the at least one machine learning procedure includes max-pooling and upsampling.
 16. The computer-accessible medium of claim 15, wherein the computer arrangement is further configured to perform the max-pooling and the upsampling using a factor of
 2. 17. The computer-accessible medium of claim 16, wherein the at least one machine learning procedure includes at least one batch normalization layer and at least one rectified linear unit layer.
 18. The computer-accessible medium of claim 1, wherein the at least one portion is at least one section of a brain of the at least one patient.
 19. A method for generating at least one gadolinium (Gd) enhanced map of at least one portion of at least one patient, comprising: receiving magnetic resonance imaging (MM) information of the at least one portion; and using a computer hardware arrangement, generating the at least one Gd enhanced map based on the MM information using at least one machine learning procedure.
 20. A system for generating at least one gadolinium (Gd) enhanced map of at least one portion of at least one patient, comprising: a computer hardware arrangement configured to: receive magnetic resonance imaging (MM) information of the at least one portion; and generate the at least one Gd enhanced map based on the MRI information using at least one machine learning procedure. 